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G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study
Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We conduct a sim...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280276/ https://www.ncbi.nlm.nih.gov/pubmed/32514028 http://dx.doi.org/10.1038/s41598-020-65917-x |
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author | Chatton, Arthur Le Borgne, Florent Leyrat, Clémence Gillaizeau, Florence Rousseau, Chloé Barbin, Laetitia Laplaud, David Léger, Maxime Giraudeau, Bruno Foucher, Yohann |
author_facet | Chatton, Arthur Le Borgne, Florent Leyrat, Clémence Gillaizeau, Florence Rousseau, Chloé Barbin, Laetitia Laplaud, David Léger, Maxime Giraudeau, Bruno Foucher, Yohann |
author_sort | Chatton, Arthur |
collection | PubMed |
description | Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We conduct a simulation study to compare the relative performance results obtained by using four different sets of covariates (those causing the outcome, those causing the treatment allocation, those causing both the outcome and the treatment allocation, and all the covariates) and four methods: g-computation, inverse probability of treatment weighting, full matching and targeted maximum likelihood estimator. Our simulations are in the context of a binary treatment, a binary outcome and baseline confounders. The simulations suggest that considering all the covariates causing the outcome led to the lowest bias and variance, particularly for g-computation. The consideration of all the covariates did not decrease the bias but significantly reduced the power. We apply these methods to two real-world examples that have clinical relevance, thereby illustrating the real-world importance of using these methods. We propose an R package RISCA to encourage the use of g-computation in causal inference. |
format | Online Article Text |
id | pubmed-7280276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72802762020-06-15 G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study Chatton, Arthur Le Borgne, Florent Leyrat, Clémence Gillaizeau, Florence Rousseau, Chloé Barbin, Laetitia Laplaud, David Léger, Maxime Giraudeau, Bruno Foucher, Yohann Sci Rep Article Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We conduct a simulation study to compare the relative performance results obtained by using four different sets of covariates (those causing the outcome, those causing the treatment allocation, those causing both the outcome and the treatment allocation, and all the covariates) and four methods: g-computation, inverse probability of treatment weighting, full matching and targeted maximum likelihood estimator. Our simulations are in the context of a binary treatment, a binary outcome and baseline confounders. The simulations suggest that considering all the covariates causing the outcome led to the lowest bias and variance, particularly for g-computation. The consideration of all the covariates did not decrease the bias but significantly reduced the power. We apply these methods to two real-world examples that have clinical relevance, thereby illustrating the real-world importance of using these methods. We propose an R package RISCA to encourage the use of g-computation in causal inference. Nature Publishing Group UK 2020-06-08 /pmc/articles/PMC7280276/ /pubmed/32514028 http://dx.doi.org/10.1038/s41598-020-65917-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chatton, Arthur Le Borgne, Florent Leyrat, Clémence Gillaizeau, Florence Rousseau, Chloé Barbin, Laetitia Laplaud, David Léger, Maxime Giraudeau, Bruno Foucher, Yohann G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
title | G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
title_full | G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
title_fullStr | G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
title_full_unstemmed | G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
title_short | G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
title_sort | g-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280276/ https://www.ncbi.nlm.nih.gov/pubmed/32514028 http://dx.doi.org/10.1038/s41598-020-65917-x |
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